{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Round Trip Tear Sheet Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When evaluating the performance of an investing strategy, it is helpful to quantify the frequency, duration, and profitability of its independent bets, or \"round trip\" trades. A round trip trade is started when a new long or short position is opened and then later completely or partially closed out.\n",
    "\n",
    "The intent of the round trip tearsheet is to help differentiate strategies that profited off a few lucky trades from strategies that profited repeatedly from genuine alpha. Breaking down round trip profitability by traded name and sector can also help inform universe selection and identify exposure risks. For example, even if your equity curve looks robust, if only two securities in your universe of fifteen names contributed to overall profitability, you may have reason to question the logic of your strategy.\n",
    "\n",
    "To identify round trips, pyfolio reconstructs the complete portfolio based on the transactions that you pass in. When you make a trade, pyfolio checks if shares are already present in your portfolio purchased at a certain price. If there are, we compute the PnL, returns and duration of that round trip trade. In calculating round trips, pyfolio will also append position closing transactions at the last timestamp in the positions data. This closing transaction will cause the PnL from any open positions to realized as completed round trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/analysis/lib/python3.11/site-packages/pyfolio/pos.py:26: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.3+186.ga0c906e.dirty\n"
     ]
    }
   ],
   "source": [
    "#import sys\n",
    "#sys.modules.pop('pyfolio', None)\n",
    "import pyfolio as pf\n",
    "print(pf.__version__)\n",
    "%matplotlib inline\n",
    "import gzip\n",
    "#import os\n",
    "import pandas as pd\n",
    "\n",
    "# silence warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "returns:  0\n",
      "2004-01-09 00:00:00+00:00   -4.282629e-13\n",
      "2004-01-12 00:00:00+00:00    8.606115e-03\n",
      "2004-01-13 00:00:00+00:00   -1.899073e-02\n",
      "2004-01-14 00:00:00+00:00    8.175663e-03\n",
      "2004-01-15 00:00:00+00:00   -3.031564e-03\n",
      "Name: 1, dtype: float64\n",
      "returns:  0\n",
      "2009-12-24 00:00:00+00:00   -0.001067\n",
      "2009-12-28 00:00:00+00:00    0.008046\n",
      "2009-12-29 00:00:00+00:00    0.007204\n",
      "2009-12-30 00:00:00+00:00    0.002646\n",
      "2009-12-31 00:00:00+00:00   -0.023011\n",
      "Name: 1, dtype: float64\n",
      "positions:                                  AMD         CERN          COST         DELL  \\\n",
      "index                                                                         \n",
      "2004-01-09 00:00:00+00:00   6961.92  21017.07875   7282.266152  21264.55188   \n",
      "2004-01-12 00:00:00+00:00  18198.58  18071.25000  17675.836401  10804.31924   \n",
      "2004-01-13 00:00:00+00:00  12060.86  11942.24625  12838.477446  16078.90380   \n",
      "2004-01-14 00:00:00+00:00  13102.40  15534.28125  14447.422640  15414.45080   \n",
      "2004-01-15 00:00:00+00:00  15518.40  14547.05000  14164.039680  14407.48813   \n",
      "\n",
      "                                    GPS          INTC           MMM  \\\n",
      "index                                                                 \n",
      "2004-01-09 00:00:00+00:00   7091.080020  21259.333890  21316.129606   \n",
      "2004-01-12 00:00:00+00:00  10685.411865  17872.477480  10882.026400   \n",
      "2004-01-13 00:00:00+00:00  16272.139000  12465.392511  12579.135758   \n",
      "2004-01-14 00:00:00+00:00  15666.440185  14884.069620  13454.542620   \n",
      "2004-01-15 00:00:00+00:00  14926.122619  14422.385864  13929.159049   \n",
      "\n",
      "                                  cash  \n",
      "index                                   \n",
      "2004-01-09 00:00:00+00:00 -6192.360298  \n",
      "2004-01-12 00:00:00+00:00 -3329.289887  \n",
      "2004-01-13 00:00:00+00:00  4708.039735  \n",
      "2004-01-14 00:00:00+00:00 -2749.470030  \n",
      "2004-01-15 00:00:00+00:00 -2462.919316  \n",
      "positions:                                  AMD         CERN          COST         DELL  \\\n",
      "index                                                                         \n",
      "2009-12-24 00:00:00+00:00  -1199.11  1316.857500  22778.660580  -3562.47039   \n",
      "2009-12-28 00:00:00+00:00    589.80   673.840032  24170.422856  -1765.41500   \n",
      "2009-12-29 00:00:00+00:00    292.50   334.920016  20993.396552    858.85252   \n",
      "2009-12-30 00:00:00+00:00   1681.56  -167.179992  34934.764512  91207.82625   \n",
      "2009-12-31 00:00:00+00:00  22254.32  9975.240484  47781.667800  53022.51955   \n",
      "\n",
      "                                    GPS          INTC           MMM  \\\n",
      "index                                                                 \n",
      "2009-12-24 00:00:00+00:00  76601.638113  36280.269375  17740.890304   \n",
      "2009-12-28 00:00:00+00:00  83143.517604  37499.607147  15692.520137   \n",
      "2009-12-29 00:00:00+00:00  94500.729990  50509.461877   7946.648597   \n",
      "2009-12-30 00:00:00+00:00  29751.588246  38052.304640  -3926.109096   \n",
      "2009-12-31 00:00:00+00:00  27393.148240  18850.582240  -1934.275491   \n",
      "\n",
      "                                   cash  \n",
      "index                                    \n",
      "2009-12-24 00:00:00+00:00  16350.679211  \n",
      "2009-12-28 00:00:00+00:00   7641.201795  \n",
      "2009-12-29 00:00:00+00:00  -6583.290764  \n",
      "2009-12-30 00:00:00+00:00 -22234.787956  \n",
      "2009-12-31 00:00:00+00:00 -11938.952799  \n",
      "positions dtypes:  AMD     float64\n",
      "CERN    float64\n",
      "COST    float64\n",
      "DELL    float64\n",
      "GPS     float64\n",
      "INTC    float64\n",
      "MMM     float64\n",
      "cash    float64\n",
      "dtype: object\n",
      "transactions:                             amount      price  sid symbol   txn_dollars\n",
      "2004-01-09 00:00:00+00:00     448  15.540000    0    AMD  -6961.920000\n",
      "2004-01-09 00:00:00+00:00    4357   4.823750    1   CERN -21017.078750\n",
      "2004-01-09 00:00:00+00:00     241  30.216872    2   COST  -7282.266152\n",
      "2004-01-09 00:00:00+00:00     618  34.408660    3   DELL -21264.551880\n",
      "2004-01-09 00:00:00+00:00     436  16.263945    4    GPS  -7091.080020\n",
      "transactions:                             amount      price  sid symbol   txn_dollars\n",
      "2009-12-31 00:00:00+00:00     267  50.033160    2   COST -13358.853720\n",
      "2009-12-31 00:00:00+00:00   -2558  13.891150    3   DELL  35533.561700\n",
      "2009-12-31 00:00:00+00:00    -103  18.685640    4    GPS   1924.620920\n",
      "2009-12-31 00:00:00+00:00   -1120  16.830877    5   INTC  18850.582240\n",
      "2009-12-31 00:00:00+00:00      27  71.639833    6    MMM  -1934.275491\n",
      "transactions:  amount           int64\n",
      "price          float64\n",
      "sid              int64\n",
      "symbol          object\n",
      "txn_dollars    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "transactions = pd.read_csv(gzip.open('/Users/linda/myprojects/github/pyfolio/pyfolio/tests/test_data/test_txn.csv.gz'),#c:\\\\myprojects\\\\github\\\\pyfolio\\\\pyfolio\\\\tests\\\\test_data\\\\\n",
    "                    index_col=0, parse_dates=True)\n",
    "positions = pd.read_csv(gzip.open('/Users/linda/myprojects/github/pyfolio/pyfolio/tests/test_data/test_pos.csv.gz'),#\n",
    "                    index_col=0, parse_dates=True)\n",
    "returns = pd.read_csv(gzip.open('/Users/linda/myprojects/github/pyfolio/pyfolio/tests/test_data/test_returns.csv.gz'),\n",
    "                      index_col=0, parse_dates=True, header=None)[1]\n",
    "returns.columns = ['returns']\n",
    "returns = returns[returns.index >= positions.index[0]]\n",
    "transactions_sid_0 = transactions[transactions['sid'] == 0]\n",
    "#print('shape of returns: ', returns.shape)\n",
    "#print('shape of positions: ', positions.shape)\n",
    "#print('shape of transactions: ', transactions.shape)\n",
    "print('returns: ', returns.head())\n",
    "print('returns: ', returns.tail())\n",
    "print('positions: ', positions.head())\n",
    "print('positions: ', positions.tail())\n",
    "print('positions dtypes: ', positions.dtypes)\n",
    "print('transactions: ', transactions.head())\n",
    "print('transactions: ', transactions.tail())\n",
    "print('transactions: ', transactions.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Sector mappings may be passed in as a dict or pd.Series. If a mapping is\n",
    "# provided, PnL from symbols with mappings will be summed to display profitability by sector.\n",
    "sect_map = {'COST': 'Consumer Goods', 'INTC':'Technology', 'CERN':'Healthcare', 'GPS':'Technology',\n",
    "            'MMM': 'Construction', 'DELL': 'Technology', 'AMD':'Technology'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to run the analysis is to call `pyfolio.create_round_trip_tear_sheet()`. Passing in a sector map is optional. You can also pass `round_trips=True` to `pyfolio.create_full_tear_sheet()` to have this be created along all the other analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>总览指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>总交易数</th>\n",
       "      <td>5819.00</td>\n",
       "      <td>1155.00</td>\n",
       "      <td>4664.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利百分比</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易数</th>\n",
       "      <td>2887.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>2292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易数</th>\n",
       "      <td>2914.00</td>\n",
       "      <td>553.00</td>\n",
       "      <td>2361.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平局交易数</th>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>盈亏指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>总净利润</th>\n",
       "      <td>67003.94</td>\n",
       "      <td>3531.32</td>\n",
       "      <td>63472.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总盈利</th>\n",
       "      <td>448674.42</td>\n",
       "      <td>20579.67</td>\n",
       "      <td>428094.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总亏损</th>\n",
       "      <td>-381670.48</td>\n",
       "      <td>-17048.35</td>\n",
       "      <td>-364622.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>利润因子</th>\n",
       "      <td>1.18</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有交易平均净利润</th>\n",
       "      <td>11.51</td>\n",
       "      <td>3.06</td>\n",
       "      <td>13.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易平均净利润</th>\n",
       "      <td>155.41</td>\n",
       "      <td>34.59</td>\n",
       "      <td>186.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易平均净利润</th>\n",
       "      <td>-130.98</td>\n",
       "      <td>-30.83</td>\n",
       "      <td>-154.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈亏比率</th>\n",
       "      <td>1.19</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大盈利</th>\n",
       "      <td>9500.14</td>\n",
       "      <td>1623.24</td>\n",
       "      <td>9500.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损</th>\n",
       "      <td>-22902.83</td>\n",
       "      <td>-661.29</td>\n",
       "      <td>-22902.83</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>持有期指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>平均持续时间-天</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中位数持续时间-天</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最长持续时间-天</th>\n",
       "      <td>84</td>\n",
       "      <td>13</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最短持续时间-天</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>收益指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>所有交易的平均收益</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易的平均收益</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易的平均收益</th>\n",
       "      <td>-0.11%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>-0.13%</td>\n",
       "    </tr>\n",
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       "      <th>所有交易的中位数收益</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易的中位数收益</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.03%</td>\n",
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       "      <th>亏损交易的中位数收益</th>\n",
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       "      <td>-0.02%</td>\n",
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       "    <tr>\n",
       "      <th>最大盈利交易</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>1.37%</td>\n",
       "      <td>6.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损交易</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-17.23%</td>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>交易品种指标</th>\n",
       "      <th>AMD</th>\n",
       "      <th>CERN</th>\n",
       "      <th>COST</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GPS</th>\n",
       "      <th>INTC</th>\n",
       "      <th>MMM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>所有交易的平均收益</th>\n",
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       "      <th>盈利交易的平均收益</th>\n",
       "      <td>0.20%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>0.11%</td>\n",
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       "      <th>亏损交易的平均收益</th>\n",
       "      <td>-0.19%</td>\n",
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       "      <td>-0.09%</td>\n",
       "      <td>-0.06%</td>\n",
       "      <td>-0.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有交易的中位数收益</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易的中位数收益</th>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易的中位数收益</th>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大盈利交易</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.14%</td>\n",
       "      <td>3.96%</td>\n",
       "      <td>2.78%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>2.40%</td>\n",
       "      <td>2.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损交易</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-3.92%</td>\n",
       "      <td>-2.32%</td>\n",
       "      <td>-6.39%</td>\n",
       "      <td>-6.86%</td>\n",
       "      <td>-4.45%</td>\n",
       "      <td>-1.79%</td>\n",
       "    </tr>\n",
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       "      <th>每个交易品种的净利润占比</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>39.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTC</th>\n",
       "      <td>37.19%</td>\n",
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       "    <tr>\n",
       "      <th>CERN</th>\n",
       "      <td>31.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>21.58%</td>\n",
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       "    <tr>\n",
       "      <th>GPS</th>\n",
       "      <td>4.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>-6.24%</td>\n",
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       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>-28.03%</td>\n",
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       "      <th>Consumer Goods</th>\n",
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",
      "text/plain": [
       "<Figure size 1400x1800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.create_round_trip_tear_sheet(returns, positions, transactions, sector_mappings=sect_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, several functions are being called. `extract_round_trips()` does the portfolio reconstruction and creates the round-trip trades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rts = pf.round_trips.extract_round_trips(transactions, \n",
    "                                         portfolio_value=positions.sum(axis='columns') / (returns + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pnl</th>\n",
       "      <th>open_dt</th>\n",
       "      <th>close_dt</th>\n",
       "      <th>long</th>\n",
       "      <th>rt_returns</th>\n",
       "      <th>symbol</th>\n",
       "      <th>duration</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-126.000000</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-13 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.022523</td>\n",
       "      <td>AMD</td>\n",
       "      <td>4 days</td>\n",
       "      <td>-0.001249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.020000</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-16 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.078507</td>\n",
       "      <td>AMD</td>\n",
       "      <td>7 days</td>\n",
       "      <td>0.000503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1540.099065</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.104696</td>\n",
       "      <td>AMD</td>\n",
       "      <td>11 days</td>\n",
       "      <td>0.015257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>287.119806</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>2004-01-21 00:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.085155</td>\n",
       "      <td>AMD</td>\n",
       "      <td>1 days</td>\n",
       "      <td>0.002861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.349947</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>2004-01-22 00:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.112198</td>\n",
       "      <td>AMD</td>\n",
       "      <td>2 days</td>\n",
       "      <td>0.001032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pnl                   open_dt                  close_dt   long  \\\n",
       "0  -126.000000 2004-01-09 00:00:00+00:00 2004-01-13 00:00:00+00:00   True   \n",
       "1    50.020000 2004-01-09 00:00:00+00:00 2004-01-16 00:00:00+00:00   True   \n",
       "2  1540.099065 2004-01-09 00:00:00+00:00 2004-01-20 00:00:00+00:00   True   \n",
       "3   287.119806 2004-01-20 00:00:00+00:00 2004-01-21 00:00:00+00:00  False   \n",
       "4   103.349947 2004-01-20 00:00:00+00:00 2004-01-22 00:00:00+00:00  False   \n",
       "\n",
       "   rt_returns symbol duration   returns  \n",
       "0   -0.022523    AMD   4 days -0.001249  \n",
       "1    0.078507    AMD   7 days  0.000503  \n",
       "2    0.104696    AMD  11 days  0.015257  \n",
       "3    0.085155    AMD   1 days  0.002861  \n",
       "4    0.112198    AMD   2 days  0.001032  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>总览指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>总交易数</th>\n",
       "      <td>5819.00</td>\n",
       "      <td>1155.00</td>\n",
       "      <td>4664.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利百分比</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易数</th>\n",
       "      <td>2887.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>2292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易数</th>\n",
       "      <td>2914.00</td>\n",
       "      <td>553.00</td>\n",
       "      <td>2361.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平局交易数</th>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>盈亏指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>总净利润</th>\n",
       "      <td>67003.94</td>\n",
       "      <td>3531.32</td>\n",
       "      <td>63472.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总盈利</th>\n",
       "      <td>448674.42</td>\n",
       "      <td>20579.67</td>\n",
       "      <td>428094.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总亏损</th>\n",
       "      <td>-381670.48</td>\n",
       "      <td>-17048.35</td>\n",
       "      <td>-364622.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>利润因子</th>\n",
       "      <td>1.18</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有交易平均净利润</th>\n",
       "      <td>11.51</td>\n",
       "      <td>3.06</td>\n",
       "      <td>13.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易平均净利润</th>\n",
       "      <td>155.41</td>\n",
       "      <td>34.59</td>\n",
       "      <td>186.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易平均净利润</th>\n",
       "      <td>-130.98</td>\n",
       "      <td>-30.83</td>\n",
       "      <td>-154.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈亏比率</th>\n",
       "      <td>1.19</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大盈利</th>\n",
       "      <td>9500.14</td>\n",
       "      <td>1623.24</td>\n",
       "      <td>9500.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损</th>\n",
       "      <td>-22902.83</td>\n",
       "      <td>-661.29</td>\n",
       "      <td>-22902.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>持有期指标</th>\n",
       "      <th>所有交易</th>\n",
       "      <th>做空交易</th>\n",
       "      <th>做多交易</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>平均持续时间-天</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中位数持续时间-天</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最长持续时间-天</th>\n",
       "      <td>84</td>\n",
       "      <td>13</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最短持续时间-天</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>所有交易的平均收益</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.01%</td>\n",
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       "    <tr>\n",
       "      <th>盈利交易的平均收益</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.15%</td>\n",
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       "    <tr>\n",
       "      <th>亏损交易的平均收益</th>\n",
       "      <td>-0.11%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>-0.13%</td>\n",
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       "    <tr>\n",
       "      <th>所有交易的中位数收益</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
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       "    <tr>\n",
       "      <th>盈利交易的中位数收益</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.03%</td>\n",
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       "    <tr>\n",
       "      <th>亏损交易的中位数收益</th>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大盈利交易</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>1.37%</td>\n",
       "      <td>6.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损交易</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-0.72%</td>\n",
       "      <td>-17.23%</td>\n",
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       "      <th>交易品种指标</th>\n",
       "      <th>AMD</th>\n",
       "      <th>CERN</th>\n",
       "      <th>COST</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GPS</th>\n",
       "      <th>INTC</th>\n",
       "      <th>MMM</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>所有交易的平均收益</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
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       "    <tr>\n",
       "      <th>盈利交易的平均收益</th>\n",
       "      <td>0.20%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>0.10%</td>\n",
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       "    <tr>\n",
       "      <th>亏损交易的平均收益</th>\n",
       "      <td>-0.19%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.15%</td>\n",
       "      <td>-0.09%</td>\n",
       "      <td>-0.06%</td>\n",
       "      <td>-0.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有交易的中位数收益</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盈利交易的中位数收益</th>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>亏损交易的中位数收益</th>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大盈利交易</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.14%</td>\n",
       "      <td>3.96%</td>\n",
       "      <td>2.78%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>2.40%</td>\n",
       "      <td>2.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最大亏损交易</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-3.92%</td>\n",
       "      <td>-2.32%</td>\n",
       "      <td>-6.39%</td>\n",
       "      <td>-6.86%</td>\n",
       "      <td>-4.45%</td>\n",
       "      <td>-1.79%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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